Alim24288 Vol. 23 No. 3 (2024)

Vol. 23, No. 3 (2024), Alim24288 https://doi.org/10.24275/rmiq/Alim24288

Comparison of mathematical and artificial neural network model to predict hot air-drying kinetics of garlic slices and determination of powder qualities

Authors

L.T.K. Loan and B.T. Vinh

Abstract

It is difficult to model the changes in moisture content when using hot air dryers that vary in temperature. This study focused on examining mathematical models and artificial neural networks (ANN) to forecast the moisture content of sliced garlic. Additionally, the study explored the impact of drying temperature on sliced garlic (2 mm). The trials were conducted using four different degrees of hot air temperature (50, 60, 70, and 80ºC). The quality of the powder used in these treatments was also assessed. The results indicated that out of the seven mathematical models, the two-term model provided the most accurate prediction of moisture ratio during the drying process, as evidenced by its greatest R-square value and lowest MSE. In addition, an artificial neural network (ANN) model with 4 hidden layers can also yield the most accurate model, meeting the same criteria as the mathematical model. When comparing the ANN model to the other model, both are capable of providing highly accurate predictions. Nevertheless, the use of an ANN model could yield more advantages in the up-scaling process. Furthermore, subjecting garlic slices to a drying process at a temperature of 60oC can result in a product that possesses elevated levels of antioxidants, antioxidant activity, allicin content, and overall acceptance.

Keywords

garlic, artificial neural network, drying, modeling, antioxidant.

References
Arabhosseini, A., Huisman, W., van Boxtel, A., & Müller, J. (2009). Modeling of thin layer drying of tarragon (Artemisia dracunculus L.). Industrial Crops and Products, 29(1), 53-59. https://doi.org/10.1016/j.indcrop.2008.04.005 Caiyan, J., Shan, S., Jie, Z., Baoshang, F., Minqiang, G., Pengbo, S., & Pengfei, J. (2023). Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow. Quality Assurance and Safety of Crops & Foods, 15(1), 84-91. https://doi.org/10.15586/qas.v15i1.1085 Chokphoemphun, S., Hongkong, S., & Chokphoemphun, S. (2023). Evaluation of drying behavior and characteristics of potato slices in multi–stage convective cabinet dryer: Application of artificial neural network. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2023.06.003 Daliran, A., Taki, M., Marzban, A., Rahnama, M., & Farhadi, R. (2023). Experimental evaluation and modeling the mass and temperature of dried mint in greenhouse solar dryer; Application of machine learning method. Case Studies in Thermal Engineering, 47, 103048. https://doi.org/10.1016/j.csite.2023.103048 Dandamrongrak, R., Young, G., & Mason, R. (2002). Evaluation of various pre-treatments for the dehydration of banana and selection of suitable drying models. Journal of Food Engineering, 55(2), 139-146. https://doi.org/10.1016/S0260-8774(02)00028-6 Demiray, E., & Tulek, Y. (2017). Effect of temperature on water diffusion during rehydration of sun-dried red pepper (Capsicum annuum L.). Heat and Mass Transfer, 53, 1829-1834. https://doi.org/10.1007/s00231-016-1940-0 Dorofki, M., Elshafie, A., Jaafar, O., Karim, O., & Mastura, S. (2012). A GIS-ANN-Based Method for Calculating the Runoff. Energy, 1(1), 1-5. Dorrigiv, M., Zareiyan, A., & Hosseinzadeh, H. (2020). Garlic (Allium sativum) as an antidote or a protective agent against natural or chemical toxicities: A comprehensive update review. Phytotherapy Research, 34(8), 1770-1797. https://doi.org/10.1002/ptr.6645 El-Saber Batiha, G., Magdy Beshbishy, A., G. Wasef, L., Elewa, Y. H., A. Al-Sagan, A., Abd El-Hack, M. E., Taha, A. E., M. Abd-Elhakim, Y., & Prasad Devkota, H. (2020). Chemical constituents and pharmacological activities of garlic (Allium sativum L.): A review. Nutrients, 12(3), 872. https://doi.org/10.3390/nu12030872 Emadzadeh, B., Ghorani, B., Naji-Tabasi, S., Charpashlo, E., & Molaveisi, M. (2021). Fate of β-cyclodextrin-sugar beet pectin microcapsules containing garlic essential oil in an acidic food beverage. Food Bioscience, 42, 101029. https://doi.org/10.1016/j.fbio.2021.101029 Espinoza, T., Valencia, E., Albarrán, M., Díaz, D., Quevedo, R. A., Díaz, O., & Bastías, J. (2020). Garlic (Allium sativum L) and Its beneficial properties for health: A review. Agroindustrial Science, 10(1), 103-115. https://doi.org/10.17268/agroind.sci.2020.01.14 Guo, X., Hao, Q., Qiao, X., Li, M., Qiu, Z., Zheng, Z., & Zhang, B. (2023). An evaluation of different pretreatment methods of hot-air drying of garlic: Drying characteristics, energy consumption and quality properties. LWT, 180, 114685. https://doi.org/10.1016/j.lwt.2023.114685 Hadjout‐Krimat, L., Belbahi, A., Dahmoune, F., Hentabli, M., Boudria, A., Achat, S., Remini, H., Oukhmanou‐Bensidhoum, S., Spigno, G., & Madani, K. (2023). Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques. Journal of Food Process Engineering, 46(2), e14232. https://doi.org/10.1111/jfpe.14232 Jeevarathinam, G., Pandiselvam, R., Pandiarajan, T., Preetha, P., Krishnakumar, T., Balakrishnan, M., Thirupathi, V., Ganapathy, S., & Amirtham, D. (2022). Design, development, and drying kinetics of infrared-assisted hot air dryer for turmeric slices. Journal of Food Process Engineering, 45(6), e13876. https://doi.org/10.1111/jfpe.13876 Karlović, S., Dujmić, F., Brnčić, S. R., Sabolović, M. B., Ninčević Grassino, A., Škegro, M., Šimić, M. A., & Brnčić, M. (2023). Mathematical modeling and optimization of ultrasonic pre-treatment for drying of pumpkin (Cucurbita moschata). Processes, 11(2), 469. https://doi.org/10.3390/pr11020469 Langbauer, R., Nunner, G., Zmek, T., Klarner, J., Prieler, R., & Hochenauer, C. (2023). Modelling of thermal shrinkage of seamless steel pipes using artificial neural networks (ANN) focussing on the influence of the ANN architecture. Results in Engineering, 17, 100999. https://doi.org/10.1016/j.rineng.2023.100999 Lara-Cerecedo, L. O., Pitalúa-Díaz, N., & Hinojosa-Palafox, J. F. (2023). Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA. Revista Mexicana de Ingeniería Química, 22(1), Ener2956-Ener2956. https://doi.org/10.24275/rmiq/Ener2956 Lawson, L. D., & Wang, Z. J. (2001). Low allicin release from garlic supplements: a major problem due to the sensitivities of alliinase activity. Journal of Agricultural and Food Chemistry, 49(5), 2592-2599. https://doi.org/10.1021/jf001287m Loan, L. T. K., & Tai, N. V. (2023). Process of foam-mat drying of purple rice bran extract and evaluation of product properties. Revista Mexicana de Ingeniería Química, 22(3), Alim23148. https://doi.org/10.24275/rmiq/Alim23148 Loan, L. T. K., Thuy, N. M., & Van Tai, N. (2023). Mathematical and artificial neural network modeling of hot air drying kinetics of instant “Cẩm” brown rice. Food Science and Technology, 43, e27623. https://doi.org/10.5327/fst.27623 Mbegbu, N. N., Nwajinka, C. O., & Amaefule, D. O. (2021). Thin layer drying models and characteristics of scent leaves (Ocimum gratissimum) and lemon basil leaves (Ocimum africanum). Heliyon, 7(1), e05945. https://doi.org/10.1016/j.heliyon.2021.e05945 Mohan, I., Sahoo, A., Mandal, S., & Kumar, S. (2023). Kinetic modeling and thermogravimetric investigation of Phoenix dactylifera and Phyllanthus emblica non-edible oil seeds: artificial neural network (ANN) prediction modeling. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-023-04094-z Ozma, M. A., Abbasi, A., Ahangarzadeh Rezaee, M., Hosseini, H., Hosseinzadeh, N., Sabahi, S., Noori, S. M. A., Sepordeh, S., Khodadadi, E., & Lahouty, M. (2023). A critical review on the nutritional and medicinal profiles of garlic’s (Allium sativum L.) bioactive compounds. Food Reviews International, 39(9), 6324-6361. https://doi.org/10.1080/87559129.2022.2100417 Selvi, K. Ç., Alkhaled, A. Y., & Yıldız, T. (2022). Application of Artificial Neural Network for Predicting the Drying Kinetics and Chemical Attributes of Linden (Tilia platyphyllos Scop.) during the Infrared Drying Process. Processes, 10(10), 2069. https://doi.org/10.3390/pr10102069 Subramanyam, R., & Narayanan, M. (2023). Artificial neural network modeling for drying kinetics of paddy using a cabinet tray dryer. Chemical Industry & Chemical Engineering Quarterly, 29(2), 87-98. https://doi.org/10.2298/CICEQ220106017S Subroto, E., Cahyana, Y., Tensiska, M., Lembong, F., Filianty, E., Kurniati, E., Wulandari, D., Saputra, R., & Faturachman, F. (2021). Bioactive compounds in garlic (Allium sativum L.) as a source of antioxidants and its potential to improve the immune system: a review. Food Research, 5(1), 10.26656. https://doi.org/10.26656/fr.2017.5(6).042 Tai, N. V., Linh, M. N., & Thuy, N. M. (2021). Modeling of thin layer drying characteristics of “Xiem” banana peel cultivated at U Minh district, Ca Mau province, Vietnam. Food Research, 5(5), 244-249. http://doi.org/10.26656/fr.2017.5(5).180 Tai, N. V., Thuy, N. M., Huong, H. T. T., Dang, P. C., & Minh, V. Q. (2024). Model kinetic and techno-analysis of moringa leaves hot air-drying process for sustainability development. Acta Scientiarum Polonorum Technologia Alimentaria, 23(1), 5-14. Thuwapanichayanan, R., Prachayawarakorn, S., & Soponronnarit, S. (2014). Heat and moisture transport behaviour and quality of chopped garlic undergoing different drying methods. Journal of Food Engineering, 136, 34-41. https://doi.org/10.1016/j.jfoodeng.2014.03.017 Thuy, N. M., Giau, T. N., Hao, H. V., Dung, N. C., Tai, N. V., & Minh, V. Q. (2024). Effects of steaming and drying on quality and antioxidant activity of white-fleshed sweet potato powder (Ipomoea batatas). Revista Mexicana de Ingeniería Química, 22(3), Alim24184. Thuy, N. M., Giau, T. N., Tai, N. V., & Minh, V. Q. (2023). Drying kinetics and mathematical modeling of dried macaroni supplemented with Gac aril Cinética de secado y modelado matemático de macarrones secos suplementados con Gac aril. Revista Mexicana de Ingeniería Química, 22(3), Alim23103. https://doi.org/10.24275/rmiq/Alim23103 Thuy, N. M., Hiep, L. H., Tai, N. V., Huong, H. T. T., & Minh, V. Q. (2022). Impact of drying temperatures on drying behaviours, energy consumption and quality of purple sweet potato flour. Acta Scientiarum Polonorum Technologia Alimentaria, 21(4), 379-387. https://doi.org/10.17306/J.AFS.1061 Thuy, N. M., Minh, V. Q., Ha, H. T. N., & Tai, N. V. (2021). Impact of different thin layer drying temperatures on the drying time and quality of butterfly pea flowers. Food Research, 5, 197-203. https://doi.org/10.26656/fr.2017.5(6).328 Van Tai, N., Linh, M. N., & Thuy, N. M. (2021). Optimization of extraction conditions of phytochemical compounds in “Xiem” banana peel powder using response surface methodology. Journal of Applied Biology and Biotechnology, 9(6), 56-62. Wongsa, P., Bhuyar, P., Tongkoom, K., Spreer, W., & Müller, J. (2023). Influence of hot-air drying methods on the phenolic compounds/allicin content, antioxidant activity and α-amylase/α-glucosidase inhibition of garlic (Allium sativum L.). European Food Research and Technology, 249(2), 523-535. https://doi.org/10.1007/s00217-022-04150-4 Yang, T., Zheng, X., Vidyarthi, S. K., Xiao, H., Yao, X., Li, Y., Zang, Y., & Zhang, J. (2023). Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking. Foods, 12(9), 1897. https://doi.org/10.3390/foods12091897 Zalpouri, R., Singh, M., Kaur, P., Kaur, A., Gaikwad, K. K., & Singh, A. (2023). Drying Kinetics, Physicochemical and Thermal Analysis of Onion Puree Dried Using a Refractance Window Dryer. Processes, 11(3), 700. https://doi.org/10.3390/pr11030700 Zhang, B., Qiu, Z., Zhao, R., Zheng, Z., Lu, X., & Qiao, X. (2021). Effect of blanching and freezing on the physical properties, bioactive compounds, and microstructure of garlic (Allium sativum L.). Journal of Food Science, 86(1), 31-39. https://doi.org/10.1111/1750-3841.15525 Zheng, Z.-A., Wang, S.-Y., Wang, H., Xiao, H., Liu, Z.-L., Pan, Y.-H., & Gao, L. (2023). Comparative Study on the Influence of Various Drying Techniques on Drying Characteristics and Physicochemical Quality of Garlic Slices. Foods, 12(6), 1314. https://doi.org/10.3390/foods12061314 Zhong, Y., Ding, Y., Jiang, G., Lu, K., & Li, C. (2023). Comparison of Artificial Neural Networks and kinetic inverse modeling to predict biomass pyrolysis behavior. Journal of Analytical and Applied Pyrolysis, 169, 105802. https://doi.org/10.1016/j.jaap.2022.105802

References

Arabhosseini, A., Huisman, W., van Boxtel, A., & Müller, J. (2009). Modeling of thin layer drying of tarragon (Artemisia dracunculus L.). Industrial Crops and Products, 29(1), 53-59. https://doi.org/10.1016/j.indcrop.2008.04.005
Caiyan, J., Shan, S., Jie, Z., Baoshang, F., Minqiang, G., Pengbo, S., & Pengfei, J. (2023). Application of an artificial neural network model to predict the change of moisture during drying of sturgeon bone marrow. Quality Assurance and Safety of Crops & Foods, 15(1), 84-91. https://doi.org/10.15586/qas.v15i1.1085
Chokphoemphun, S., Hongkong, S., & Chokphoemphun, S. (2023). Evaluation of drying behavior and characteristics of potato slices in multi–stage convective cabinet dryer: Application of artificial neural network. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2023.06.003
Daliran, A., Taki, M., Marzban, A., Rahnama, M., & Farhadi, R. (2023). Experimental evaluation and modeling the mass and temperature of dried mint in greenhouse solar dryer; Application of machine learning method. Case Studies in Thermal Engineering, 47, 103048. https://doi.org/10.1016/j.csite.2023.103048
Dandamrongrak, R., Young, G., & Mason, R. (2002). Evaluation of various pre-treatments for the dehydration of banana and selection of suitable drying models. Journal of Food Engineering, 55(2), 139-146. https://doi.org/10.1016/S0260-8774(02)00028-6
Demiray, E., & Tulek, Y. (2017). Effect of temperature on water diffusion during rehydration of sun-dried red pepper (Capsicum annuum L.). Heat and Mass Transfer, 53, 1829-1834. https://doi.org/10.1007/s00231-016-1940-0
Dorofki, M., Elshafie, A., Jaafar, O., Karim, O., & Mastura, S. (2012). A GIS-ANN-Based Method for Calculating the Runoff. Energy, 1(1), 1-5.
Dorrigiv, M., Zareiyan, A., & Hosseinzadeh, H. (2020). Garlic (Allium sativum) as an antidote or a protective agent against natural or chemical toxicities: A comprehensive update review. Phytotherapy Research, 34(8), 1770-1797. https://doi.org/10.1002/ptr.6645
El-Saber Batiha, G., Magdy Beshbishy, A., G. Wasef, L., Elewa, Y. H., A. Al-Sagan, A., Abd El-Hack, M. E., Taha, A. E., M. Abd-Elhakim, Y., & Prasad Devkota, H. (2020). Chemical constituents and pharmacological activities of garlic (Allium sativum L.): A review. Nutrients, 12(3), 872. https://doi.org/10.3390/nu12030872
Emadzadeh, B., Ghorani, B., Naji-Tabasi, S., Charpashlo, E., & Molaveisi, M. (2021). Fate of β-cyclodextrin-sugar beet pectin microcapsules containing garlic essential oil in an acidic food beverage. Food Bioscience, 42, 101029. https://doi.org/10.1016/j.fbio.2021.101029
Espinoza, T., Valencia, E., Albarrán, M., Díaz, D., Quevedo, R. A., Díaz, O., & Bastías, J. (2020). Garlic (Allium sativum L) and Its beneficial properties for health: A review. Agroindustrial Science, 10(1), 103-115. https://doi.org/10.17268/agroind.sci.2020.01.14
Guo, X., Hao, Q., Qiao, X., Li, M., Qiu, Z., Zheng, Z., & Zhang, B. (2023). An evaluation of different pretreatment methods of hot-air drying of garlic: Drying characteristics, energy consumption and quality properties. LWT, 180, 114685. https://doi.org/10.1016/j.lwt.2023.114685
Hadjout‐Krimat, L., Belbahi, A., Dahmoune, F., Hentabli, M., Boudria, A., Achat, S., Remini, H., Oukhmanou‐Bensidhoum, S., Spigno, G., & Madani, K. (2023). Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques. Journal of Food Process Engineering, 46(2), e14232. https://doi.org/10.1111/jfpe.14232
Jeevarathinam, G., Pandiselvam, R., Pandiarajan, T., Preetha, P., Krishnakumar, T., Balakrishnan, M., Thirupathi, V., Ganapathy, S., & Amirtham, D. (2022). Design, development, and drying kinetics of infrared-assisted hot air dryer for turmeric slices. Journal of Food Process Engineering, 45(6), e13876. https://doi.org/10.1111/jfpe.13876
Karlović, S., Dujmić, F., Brnčić, S. R., Sabolović, M. B., Ninčević Grassino, A., Škegro, M., Šimić, M. A., & Brnčić, M. (2023). Mathematical modeling and optimization of ultrasonic pre-treatment for drying of pumpkin (Cucurbita moschata). Processes, 11(2), 469. https://doi.org/10.3390/pr11020469
Langbauer, R., Nunner, G., Zmek, T., Klarner, J., Prieler, R., & Hochenauer, C. (2023). Modelling of thermal shrinkage of seamless steel pipes using artificial neural networks (ANN) focussing on the influence of the ANN architecture. Results in Engineering, 17, 100999. https://doi.org/10.1016/j.rineng.2023.100999
Lara-Cerecedo, L. O., Pitalúa-Díaz, N., & Hinojosa-Palafox, J. F. (2023). Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA. Revista Mexicana de Ingeniería Química, 22(1), Ener2956-Ener2956. https://doi.org/10.24275/rmiq/Ener2956
Lawson, L. D., & Wang, Z. J. (2001). Low allicin release from garlic supplements: a major problem due to the sensitivities of alliinase activity. Journal of Agricultural and Food Chemistry, 49(5), 2592-2599. https://doi.org/10.1021/jf001287m
Loan, L. T. K., & Tai, N. V. (2023). Process of foam-mat drying of purple rice bran extract and evaluation of product properties. Revista Mexicana de Ingeniería Química, 22(3), Alim23148. https://doi.org/10.24275/rmiq/Alim23148
Loan, L. T. K., Thuy, N. M., & Van Tai, N. (2023). Mathematical and artificial neural network modeling of hot air drying kinetics of instant “Cẩm” brown rice. Food Science and Technology, 43, e27623. https://doi.org/10.5327/fst.27623
Mbegbu, N. N., Nwajinka, C. O., & Amaefule, D. O. (2021). Thin layer drying models and characteristics of scent leaves (Ocimum gratissimum) and lemon basil leaves (Ocimum africanum). Heliyon, 7(1), e05945. https://doi.org/10.1016/j.heliyon.2021.e05945
Mohan, I., Sahoo, A., Mandal, S., & Kumar, S. (2023). Kinetic modeling and thermogravimetric investigation of Phoenix dactylifera and Phyllanthus emblica non-edible oil seeds: artificial neural network (ANN) prediction modeling. Biomass Conversion and Biorefinery. https://doi.org/10.1007/s13399-023-04094-z
Ozma, M. A., Abbasi, A., Ahangarzadeh Rezaee, M., Hosseini, H., Hosseinzadeh, N., Sabahi, S., Noori, S. M. A., Sepordeh, S., Khodadadi, E., & Lahouty, M. (2023). A critical review on the nutritional and medicinal profiles of garlic’s (Allium sativum L.) bioactive compounds. Food Reviews International, 39(9), 6324-6361. https://doi.org/10.1080/87559129.2022.2100417
Selvi, K. Ç., Alkhaled, A. Y., & Yıldız, T. (2022). Application of Artificial Neural Network for Predicting the Drying Kinetics and Chemical Attributes of Linden (Tilia platyphyllos Scop.) during the Infrared Drying Process. Processes, 10(10), 2069. https://doi.org/10.3390/pr10102069
Subramanyam, R., & Narayanan, M. (2023). Artificial neural network modeling for drying kinetics of paddy using a cabinet tray dryer. Chemical Industry & Chemical Engineering Quarterly, 29(2), 87-98. https://doi.org/10.2298/CICEQ220106017S
Subroto, E., Cahyana, Y., Tensiska, M., Lembong, F., Filianty, E., Kurniati, E., Wulandari, D., Saputra, R., & Faturachman, F. (2021). Bioactive compounds in garlic (Allium sativum L.) as a source of antioxidants and its potential to improve the immune system: a review. Food Research, 5(1), 10.26656. https://doi.org/10.26656/fr.2017.5(6).042
Tai, N. V., Linh, M. N., & Thuy, N. M. (2021). Modeling of thin layer drying characteristics of “Xiem” banana peel cultivated at U Minh district, Ca Mau province, Vietnam. Food Research, 5(5), 244-249. http://doi.org/10.26656/fr.2017.5(5).180
Tai, N. V., Thuy, N. M., Huong, H. T. T., Dang, P. C., & Minh, V. Q. (2024). Model kinetic and techno-analysis of moringa leaves hot air-drying process for sustainability development. Acta Scientiarum Polonorum Technologia Alimentaria, 23(1), 5-14.
Thuwapanichayanan, R., Prachayawarakorn, S., & Soponronnarit, S. (2014). Heat and moisture transport behaviour and quality of chopped garlic undergoing different drying methods. Journal of Food Engineering, 136, 34-41. https://doi.org/10.1016/j.jfoodeng.2014.03.017
Thuy, N. M., Giau, T. N., Hao, H. V., Dung, N. C., Tai, N. V., & Minh, V. Q. (2024). Effects of steaming and drying on quality and antioxidant activity of white-fleshed sweet potato powder (Ipomoea batatas). Revista Mexicana de Ingeniería Química, 22(3), Alim24184.
Thuy, N. M., Giau, T. N., Tai, N. V., & Minh, V. Q. (2023). Drying kinetics and mathematical modeling of dried macaroni supplemented with Gac aril Cinética de secado y modelado matemático de macarrones secos suplementados con Gac aril. Revista Mexicana de Ingeniería Química, 22(3), Alim23103. https://doi.org/10.24275/rmiq/Alim23103
Thuy, N. M., Hiep, L. H., Tai, N. V., Huong, H. T. T., & Minh, V. Q. (2022). Impact of drying temperatures on drying behaviours, energy consumption and quality of purple sweet potato flour. Acta Scientiarum Polonorum Technologia Alimentaria, 21(4), 379-387. https://doi.org/10.17306/J.AFS.1061
Thuy, N. M., Minh, V. Q., Ha, H. T. N., & Tai, N. V. (2021). Impact of different thin layer drying temperatures on the drying time and quality of butterfly pea flowers. Food Research, 5, 197-203. https://doi.org/10.26656/fr.2017.5(6).328
Van Tai, N., Linh, M. N., & Thuy, N. M. (2021). Optimization of extraction conditions of phytochemical compounds in “Xiem” banana peel powder using response surface methodology. Journal of Applied Biology and Biotechnology, 9(6), 56-62.
Wongsa, P., Bhuyar, P., Tongkoom, K., Spreer, W., & Müller, J. (2023). Influence of hot-air drying methods on the phenolic compounds/allicin content, antioxidant activity and α-amylase/α-glucosidase inhibition of garlic (Allium sativum L.). European Food Research and Technology, 249(2), 523-535. https://doi.org/10.1007/s00217-022-04150-4
Yang, T., Zheng, X., Vidyarthi, S. K., Xiao, H., Yao, X., Li, Y., Zang, Y., & Zhang, J. (2023). Artificial Neural Network Modeling and Genetic Algorithm Multiobjective Optimization of Process of Drying-Assisted Walnut Breaking. Foods, 12(9), 1897. https://doi.org/10.3390/foods12091897
Zalpouri, R., Singh, M., Kaur, P., Kaur, A., Gaikwad, K. K., & Singh, A. (2023). Drying Kinetics, Physicochemical and Thermal Analysis of Onion Puree Dried Using a Refractance Window Dryer. Processes, 11(3), 700. https://doi.org/10.3390/pr11030700
Zhang, B., Qiu, Z., Zhao, R., Zheng, Z., Lu, X., & Qiao, X. (2021). Effect of blanching and freezing on the physical properties, bioactive compounds, and microstructure of garlic (Allium sativum L.). Journal of Food Science, 86(1), 31-39. https://doi.org/10.1111/1750-3841.15525
Zheng, Z.-A., Wang, S.-Y., Wang, H., Xiao, H., Liu, Z.-L., Pan, Y.-H., & Gao, L. (2023). Comparative Study on the Influence of Various Drying Techniques on Drying Characteristics and Physicochemical Quality of Garlic Slices. Foods, 12(6), 1314. https://doi.org/10.3390/foods12061314
Zhong, Y., Ding, Y., Jiang, G., Lu, K., & Li, C. (2023). Comparison of Artificial Neural Networks and kinetic inverse modeling to predict biomass pyrolysis behavior. Journal of Analytical and Applied Pyrolysis, 169, 105802. https://doi.org/10.1016/j.jaap.2022.105802